import pandas as pd
df=pd.read_csv('cleaned_data.csv')
df

	SNO	Category	States_UT	poverty	malnutrition	literacy	drinking water	electricity	unemployment
0	1.0	State	Andhra Pradesh	9.2	35.5	65.6	99.81125151	100	5.7
1	2.0	State	Arunachal Pradesh	34.67	16	79.9	92.63657957	100	7.7
2	3.0	State	Assam	31.98	29.4	84.9	74.72472158	100	7.1
3	4.0	State	Bihar	33.74	38.7	64.7	96.28630888	100	10.6
4	5.0	State	Chhattisgarh	39.93	40	74.1	99.59868138	99.67	2.6
5	6.0	State	Goa	5.09	20.3	88.9	100	100	9.4
6	7.0	State	Gujarat	16.63	34.2	80.7	100	100	3.4
7	8.0	State	Haryana	11.16	28.8	77.3	99.71431711	100	9.8
8	9.0	State	Himachal Pradesh	8.06	22.6	84.2	100	100	5.8
9	10.0	State	Jharkhand	36.96	42.9	67.3	99.70510686	100	5.5
10	11.0	State	Karnataka	20.91	32	75.7	100	100	3.9
11	12.0	State	Kerala	7.05	18.7	94.6	99.35308848	100	10.4
12	13.0	State	Madhya Pradesh	31.65	38.7	70.5	99.64891263	100	3.7
13	14.0	State	Maharashtra	17.35	30.9	80.3	99.70219926	100	5.4
14	15.0	State	Manipur	36.89	13	85.6	100	100	10.1
15	16.0	State	Meghalaya	11.87	30	91.4	100	100	2.8
16	17.0	State	Mizoram	20.4	11.3	98.5	100	100	7.3
17	18.0	State	Nagaland	18.88	16.3	93.8	100	100	18.5
18	19.0	State	Odisha	32.59	29.2	72.5	98.13119756	100	7.6
19	20.0	State	Punjab	8.26	19.7	79.4	92.4089014	100	8.0
20	21.0	State	Rajasthan	14.71	31.5	67.1	92.2759537	100	6.2
21	22.0	State	Sikkim	8.19	11	86.2	100	100	3.3
22	23.0	State	Tamil Nadu	11.28	23.5	80.7	99.39444338	100	7.2
23	24.0	State	Telangana	Null	30.8	67.4	100	100	8.8
24	25.0	State	Tripura	14.05	23.8	89.9	84.84415213	100	10.5
25	26.0	State	Uttar Pradesh	29.43	36.8	68.2	99.62768688	100	6.2
26	27.0	State	Uttarakhand	11.26	18.7	79.0	99.26236621	100	9.5
27	28.0	State	West Bengal	19.98	30.9	79.0	95.47820993	100	4.1
28	29.0	Union Territory	Andaman and Nicobar Islands	1	Null	88.3	100	Null	13.8
29	30.0	Union Territory	Chandigarh	21.81	Null	89.1	Null	Null	7.8
30	31.0	Union Territory	Dadra and Nagar Haveli	39.31	Null	77.7	Null	Null	1.5
31	32.0	Union Territory	Daman and Diu	9.86	Null	88.3	Null	Null	0.0
32	33.0	Union Territory	Delhi	9.91	28.1	86.4	Null	100	10.7
33	34.0	Union Territory	Jammu and Kashmir	10.35	13	76.4	99.889989	100	5.6
34	35.0	Union Territory	Ladakh	10.35	13	76.4	100	100	5.6
35	36.0	Union Territory	Lakshadweep	2.77	Null	95.7	Null	Null	32.0
36	37.0	Union Territory	Puducherry	9.69	Null	89.5	95.88550984	100	8.7
37	NaN	NaN	India	21.92	33.4	74.6	97.44222839	99.99	6.2
38	NaN	NaN	Target	10.96	1.9	100.0	100	100	3.0

df.describe()

	SNO	literacy	unemployment
count	37.000000	39.000000	39.000000
mean	19.000000	81.276923	7.589744
std	10.824355	9.510699	5.331623
min	1.000000	64.700000	0.000000
25%	10.000000	75.150000	4.750000
50%	19.000000	80.300000	7.100000
75%	28.000000	88.600000	9.450000
max	37.000000	100.000000	32.000000

import plotly.express as px
fig = px.box(df, y="poverty")
fig.show()

fig = px.box(df, y="malnutrition")
fig.show()

fig = px.box(df, y="literacy")
fig.show()

fig = px.box(df, y="drinking water")
fig.show()

fig = px.box(df, y="electricity")
fig.show()

fig = px.box(df, y="unemployment")
fig.show()

import plotly.graph_objs as go
desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

parameters = ["poverty",
              "malnutrition",
              "literacy",
              "drinking water",
              "electricity",
              "unemployment"]


for i in range(1, 7):
    print(i, parameters[i-1])

n=1

parameter = parameters[n-1]

x = list()
y = list()

for i in range(37):
    val = df.loc[i, parameter]
    if(val != 'Null'):
        x.append(df.loc[i, 'States_UT'])
        y.append(round(float(df.loc[i, parameter]), 2))


india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.add_hline(y=target, line_color="red", annotation_text="Target",
              annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
              annotation_position="bottom right")

fig.update_layout(
    go.Layout(
        title=desc[parameter],
        xaxis=dict(
            title='states/UT'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 poverty
2 malnutrition
3 literacy
4 drinking water
5 electricity
6 unemployment

import plotly.graph_objs as go
desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

parameters = ["poverty",
              "malnutrition",
              "literacy",
              "drinking water",
              "electricity",
              "unemployment"]


for i in range(1, 7):
    print(i, parameters[i-1])

n=2

parameter = parameters[n-1]

x = list()
y = list()

for i in range(37):
    val = df.loc[i, parameter]
    if(val != 'Null'):
        x.append(df.loc[i, 'States_UT'])
        y.append(round(float(df.loc[i, parameter]), 2))


india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.add_hline(y=target, line_color="red", annotation_text="Target",
              annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
              annotation_position="bottom right")

fig.update_layout(
    go.Layout(
        title=desc[parameter],
        xaxis=dict(
            title='states/UT'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 poverty
2 malnutrition
3 literacy
4 drinking water
5 electricity
6 unemployment

import plotly.graph_objs as go
desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

parameters = ["poverty",
              "malnutrition",
              "literacy",
              "drinking water",
              "electricity",
              "unemployment"]


for i in range(1, 7):
    print(i, parameters[i-1])

n=3

parameter = parameters[n-1]

x = list()
y = list()

for i in range(37):
    val = df.loc[i, parameter]
    if(val != 'Null'):
        x.append(df.loc[i, 'States_UT'])
        y.append(round(float(df.loc[i, parameter]), 2))


india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.add_hline(y=target, line_color="red", annotation_text="Target",
              annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
              annotation_position="bottom right")

fig.update_layout(
    go.Layout(
        title=desc[parameter],
        xaxis=dict(
            title='states/UT'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 poverty
2 malnutrition
3 literacy
4 drinking water
5 electricity
6 unemployment

import plotly.graph_objs as go
desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

parameters = ["poverty",
              "malnutrition",
              "literacy",
              "drinking water",
              "electricity",
              "unemployment"]


for i in range(1, 7):
    print(i, parameters[i-1])

n=4

parameter = parameters[n-1]

x = list()
y = list()

for i in range(37):
    val = df.loc[i, parameter]
    if(val != 'Null'):
        x.append(df.loc[i, 'States_UT'])
        y.append(round(float(df.loc[i, parameter]), 2))


india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.add_hline(y=target, line_color="red", annotation_text="Target",
              annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
              annotation_position="bottom right")

fig.update_layout(
    go.Layout(
        title=desc[parameter],
        xaxis=dict(
            title='states/UT'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 poverty
2 malnutrition
3 literacy
4 drinking water
5 electricity
6 unemployment

import plotly.graph_objs as go
desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

parameters = ["poverty",
              "malnutrition",
              "literacy",
              "drinking water",
              "electricity",
              "unemployment"]


for i in range(1, 7):
    print(i, parameters[i-1])

n=5

parameter = parameters[n-1]

x = list()
y = list()

for i in range(37):
    val = df.loc[i, parameter]
    if(val != 'Null'):
        x.append(df.loc[i, 'States_UT'])
        y.append(round(float(df.loc[i, parameter]), 2))


india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.add_hline(y=target, line_color="red", annotation_text="Target",
              annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
              annotation_position="bottom right")

fig.update_layout(
    go.Layout(
        title=desc[parameter],
        xaxis=dict(
            title='states/UT'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 poverty
2 malnutrition
3 literacy
4 drinking water
5 electricity
6 unemployment

import plotly.graph_objs as go
desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

parameters = ["poverty",
              "malnutrition",
              "literacy",
              "drinking water",
              "electricity",
              "unemployment"]


for i in range(1, 7):
    print(i, parameters[i-1])

n=6

parameter = parameters[n-1]

x = list()
y = list()

for i in range(37):
    val = df.loc[i, parameter]
    if(val != 'Null'):
        x.append(df.loc[i, 'States_UT'])
        y.append(round(float(df.loc[i, parameter]), 2))


india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.add_hline(y=target, line_color="red", annotation_text="Target",
              annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
              annotation_position="bottom right")

fig.update_layout(
    go.Layout(
        title=desc[parameter],
        xaxis=dict(
            title='states/UT'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 poverty
2 malnutrition
3 literacy
4 drinking water
5 electricity
6 unemployment

df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)

desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

L = ["Andhra Pradesh",
     "Arunachal Pradesh",
     "Assam",
     "Bihar",
     "Chhattisgarh",
     "Goa",
     "Gujarat",
     "Haryana",
     "Himachal Pradesh",
     "Jharkhand",
     "Karnataka",
     "Kerala",
     "Madhya Pradesh",
     "Maharashtra",
     "Manipur",
     "Meghalaya",
     "Mizoram",
     "Nagaland",
     "Odisha",
     "Punjab",
     "Rajasthan",
     "Sikkim",
     "Tamil Nadu",
     "Telangana",
     "Tripura",
     "Uttar Pradesh",
     "Uttarakhand",
     "West Bengal",
     "Andaman and Nicobar Islands",
     "Chandigarh",
     "Dadra and Nagar Haveli",
     "Daman and Diu",
     "Delhi",
     "Jammu and Kashmir",
     "Ladakh",
     "Lakshadweep",
     "Puducherry"]

for i in range(1, len(L)+1):
    print(i, L[i-1])

n = 1

name = L[n-1]

x = list()
y = list()

for key in desc:
    val = df.loc[name, key]
    if(val == 'Null'):
        continue
    else:
        y.append(round(float(val), 2))
        x.append(key)

df = pd.DataFrame(dict(
    r=y,
    theta=x))

fig = px.line_polar(df, r='r', theta='theta', line_close=True, title=name)
fig.update_traces(fill='toself')
fig.show()

1 Andhra Pradesh
2 Arunachal Pradesh
3 Assam
4 Bihar
5 Chhattisgarh
6 Goa
7 Gujarat
8 Haryana
9 Himachal Pradesh
10 Jharkhand
11 Karnataka
12 Kerala
13 Madhya Pradesh
14 Maharashtra
15 Manipur
16 Meghalaya
17 Mizoram
18 Nagaland
19 Odisha
20 Punjab
21 Rajasthan
22 Sikkim
23 Tamil Nadu
24 Telangana
25 Tripura
26 Uttar Pradesh
27 Uttarakhand
28 West Bengal
29 Andaman and Nicobar Islands
30 Chandigarh
31 Dadra and Nagar Haveli
32 Daman and Diu
33 Delhi
34 Jammu and Kashmir
35 Ladakh
36 Lakshadweep
37 Puducherry

df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)

desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

L = ["Andhra Pradesh",
     "Arunachal Pradesh",
     "Assam",
     "Bihar",
     "Chhattisgarh",
     "Goa",
     "Gujarat",
     "Haryana",
     "Himachal Pradesh",
     "Jharkhand",
     "Karnataka",
     "Kerala",
     "Madhya Pradesh",
     "Maharashtra",
     "Manipur",
     "Meghalaya",
     "Mizoram",
     "Nagaland",
     "Odisha",
     "Punjab",
     "Rajasthan",
     "Sikkim",
     "Tamil Nadu",
     "Telangana",
     "Tripura",
     "Uttar Pradesh",
     "Uttarakhand",
     "West Bengal",
     "Andaman and Nicobar Islands",
     "Chandigarh",
     "Dadra and Nagar Haveli",
     "Daman and Diu",
     "Delhi",
     "Jammu and Kashmir",
     "Ladakh",
     "Lakshadweep",
     "Puducherry"]

for i in range(1, len(L)+1):
    print(i, L[i-1])

n = 4

name = L[n-1]

x = list()
y = list()

for key in desc:
    val = df.loc[name, key]
    if(val == 'Null'):
        continue
    else:
        y.append(round(float(val), 2))
        x.append(key)

df = pd.DataFrame(dict(
    r=y,
    theta=x))

fig = px.line_polar(df, r='r', theta='theta', line_close=True, title=name)
fig.update_traces(fill='toself')
fig.show()

1 Andhra Pradesh
2 Arunachal Pradesh
3 Assam
4 Bihar
5 Chhattisgarh
6 Goa
7 Gujarat
8 Haryana
9 Himachal Pradesh
10 Jharkhand
11 Karnataka
12 Kerala
13 Madhya Pradesh
14 Maharashtra
15 Manipur
16 Meghalaya
17 Mizoram
18 Nagaland
19 Odisha
20 Punjab
21 Rajasthan
22 Sikkim
23 Tamil Nadu
24 Telangana
25 Tripura
26 Uttar Pradesh
27 Uttarakhand
28 West Bengal
29 Andaman and Nicobar Islands
30 Chandigarh
31 Dadra and Nagar Haveli
32 Daman and Diu
33 Delhi
34 Jammu and Kashmir
35 Ladakh
36 Lakshadweep
37 Puducherry

df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)

desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}

L = ["Andhra Pradesh",
     "Arunachal Pradesh",
     "Assam",
     "Bihar",
     "Chhattisgarh",
     "Goa",
     "Gujarat",
     "Haryana",
     "Himachal Pradesh",
     "Jharkhand",
     "Karnataka",
     "Kerala",
     "Madhya Pradesh",
     "Maharashtra",
     "Manipur",
     "Meghalaya",
     "Mizoram",
     "Nagaland",
     "Odisha",
     "Punjab",
     "Rajasthan",
     "Sikkim",
     "Tamil Nadu",
     "Telangana",
     "Tripura",
     "Uttar Pradesh",
     "Uttarakhand",
     "West Bengal",
     "Andaman and Nicobar Islands",
     "Chandigarh",
     "Dadra and Nagar Haveli",
     "Daman and Diu",
     "Delhi",
     "Jammu and Kashmir",
     "Ladakh",
     "Lakshadweep",
     "Puducherry"]

for i in range(1, len(L)+1):
    print(i, L[i-1])

n = 26

name = L[n-1]

x = list()
y = list()

for key in desc:
    val = df.loc[name, key]
    if(val == 'Null'):
        continue
    else:
        y.append(round(float(val), 2))
        x.append(key)

df = pd.DataFrame(dict(
    r=y,
    theta=x))

fig = px.line_polar(df, r='r', theta='theta', line_close=True, title=name)
fig.update_traces(fill='toself')
fig.show()

1 Andhra Pradesh
2 Arunachal Pradesh
3 Assam
4 Bihar
5 Chhattisgarh
6 Goa
7 Gujarat
8 Haryana
9 Himachal Pradesh
10 Jharkhand
11 Karnataka
12 Kerala
13 Madhya Pradesh
14 Maharashtra
15 Manipur
16 Meghalaya
17 Mizoram
18 Nagaland
19 Odisha
20 Punjab
21 Rajasthan
22 Sikkim
23 Tamil Nadu
24 Telangana
25 Tripura
26 Uttar Pradesh
27 Uttarakhand
28 West Bengal
29 Andaman and Nicobar Islands
30 Chandigarh
31 Dadra and Nagar Haveli
32 Daman and Diu
33 Delhi
34 Jammu and Kashmir
35 Ladakh
36 Lakshadweep
37 Puducherry

import pandas as pd
import plotly.graph_objs as go

df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)

desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}


L = ["Andhra Pradesh",
     "Arunachal Pradesh",
     "Assam",
     "Bihar",
     "Chhattisgarh",
     "Goa",
     "Gujarat",
     "Haryana",
     "Himachal Pradesh",
     "Jharkhand",
     "Karnataka",
     "Kerala",
     "Madhya Pradesh",
     "Maharashtra",
     "Manipur",
     "Meghalaya",
     "Mizoram",
     "Nagaland",
     "Odisha",
     "Punjab",
     "Rajasthan",
     "Sikkim",
     "Tamil Nadu",
     "Telangana",
     "Tripura",
     "Uttar Pradesh",
     "Uttarakhand",
     "West Bengal",
     "Andaman and Nicobar Islands",
     "Chandigarh",
     "Dadra and Nagar Haveli",
     "Daman and Diu",
     "Delhi",
     "Jammu and Kashmir",
     "Ladakh",
     "Lakshadweep",
     "Puducherry"]

for i in range(1, len(L)+1):
    print(i, L[i-1])

n = 1

name = L[n-1]

x = list()
y = list()

for key in desc:
    val = df.loc[name, key]
    if(val == 'Null'):
        continue
    else:
        x.append(key)
        y.append(round(float(val), 2))

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.update_layout(
    go.Layout(
        title=name,
        xaxis=dict(
            title='parameters'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 Andhra Pradesh
2 Arunachal Pradesh
3 Assam
4 Bihar
5 Chhattisgarh
6 Goa
7 Gujarat
8 Haryana
9 Himachal Pradesh
10 Jharkhand
11 Karnataka
12 Kerala
13 Madhya Pradesh
14 Maharashtra
15 Manipur
16 Meghalaya
17 Mizoram
18 Nagaland
19 Odisha
20 Punjab
21 Rajasthan
22 Sikkim
23 Tamil Nadu
24 Telangana
25 Tripura
26 Uttar Pradesh
27 Uttarakhand
28 West Bengal
29 Andaman and Nicobar Islands
30 Chandigarh
31 Dadra and Nagar Haveli
32 Daman and Diu
33 Delhi
34 Jammu and Kashmir
35 Ladakh
36 Lakshadweep
37 Puducherry

import pandas as pd
import plotly.graph_objs as go

df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)

desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}


L = ["Andhra Pradesh",
     "Arunachal Pradesh",
     "Assam",
     "Bihar",
     "Chhattisgarh",
     "Goa",
     "Gujarat",
     "Haryana",
     "Himachal Pradesh",
     "Jharkhand",
     "Karnataka",
     "Kerala",
     "Madhya Pradesh",
     "Maharashtra",
     "Manipur",
     "Meghalaya",
     "Mizoram",
     "Nagaland",
     "Odisha",
     "Punjab",
     "Rajasthan",
     "Sikkim",
     "Tamil Nadu",
     "Telangana",
     "Tripura",
     "Uttar Pradesh",
     "Uttarakhand",
     "West Bengal",
     "Andaman and Nicobar Islands",
     "Chandigarh",
     "Dadra and Nagar Haveli",
     "Daman and Diu",
     "Delhi",
     "Jammu and Kashmir",
     "Ladakh",
     "Lakshadweep",
     "Puducherry"]

for i in range(1, len(L)+1):
    print(i, L[i-1])

n = 4

name = L[n-1]

x = list()
y = list()

for key in desc:
    val = df.loc[name, key]
    if(val == 'Null'):
        continue
    else:
        x.append(key)
        y.append(round(float(val), 2))

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.update_layout(
    go.Layout(
        title=name,
        xaxis=dict(
            title='parameters'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 Andhra Pradesh
2 Arunachal Pradesh
3 Assam
4 Bihar
5 Chhattisgarh
6 Goa
7 Gujarat
8 Haryana
9 Himachal Pradesh
10 Jharkhand
11 Karnataka
12 Kerala
13 Madhya Pradesh
14 Maharashtra
15 Manipur
16 Meghalaya
17 Mizoram
18 Nagaland
19 Odisha
20 Punjab
21 Rajasthan
22 Sikkim
23 Tamil Nadu
24 Telangana
25 Tripura
26 Uttar Pradesh
27 Uttarakhand
28 West Bengal
29 Andaman and Nicobar Islands
30 Chandigarh
31 Dadra and Nagar Haveli
32 Daman and Diu
33 Delhi
34 Jammu and Kashmir
35 Ladakh
36 Lakshadweep
37 Puducherry

import pandas as pd
import plotly.graph_objs as go

df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)

desc = {
    "poverty": "Percentage of population living below the national poverty line",
    "malnutrition": "Percentage of children under five years who are underweight",
    "literacy": "Percentage of persons (15 years and above) who are literate",
    "drinking water": "Percentage of rural population having improved source of drinking water",
    "electricity": "Percentage of households electrified ",
    "unemployment": "Unemployment rate (%) (15-59 years)",
}


L = ["Andhra Pradesh",
     "Arunachal Pradesh",
     "Assam",
     "Bihar",
     "Chhattisgarh",
     "Goa",
     "Gujarat",
     "Haryana",
     "Himachal Pradesh",
     "Jharkhand",
     "Karnataka",
     "Kerala",
     "Madhya Pradesh",
     "Maharashtra",
     "Manipur",
     "Meghalaya",
     "Mizoram",
     "Nagaland",
     "Odisha",
     "Punjab",
     "Rajasthan",
     "Sikkim",
     "Tamil Nadu",
     "Telangana",
     "Tripura",
     "Uttar Pradesh",
     "Uttarakhand",
     "West Bengal",
     "Andaman and Nicobar Islands",
     "Chandigarh",
     "Dadra and Nagar Haveli",
     "Daman and Diu",
     "Delhi",
     "Jammu and Kashmir",
     "Ladakh",
     "Lakshadweep",
     "Puducherry"]

for i in range(1, len(L)+1):
    print(i, L[i-1])

n = 26

name = L[n-1]

x = list()
y = list()

for key in desc:
    val = df.loc[name, key]
    if(val == 'Null'):
        continue
    else:
        x.append(key)
        y.append(round(float(val), 2))

fig = go.Figure()

fig.add_trace(
    go.Bar(
        x=x,
        y=y,
        marker=dict(color=y,
                    colorscale='viridis', showscale=True)
    )
)

fig.update_layout(
    go.Layout(
        title=name,
        xaxis=dict(
            title='parameters'
        ),
        yaxis=dict(
            title='value(in %)',
        )
    )
)

fig.show()

1 Andhra Pradesh
2 Arunachal Pradesh
3 Assam
4 Bihar
5 Chhattisgarh
6 Goa
7 Gujarat
8 Haryana
9 Himachal Pradesh
10 Jharkhand
11 Karnataka
12 Kerala
13 Madhya Pradesh
14 Maharashtra
15 Manipur
16 Meghalaya
17 Mizoram
18 Nagaland
19 Odisha
20 Punjab
21 Rajasthan
22 Sikkim
23 Tamil Nadu
24 Telangana
25 Tripura
26 Uttar Pradesh
27 Uttarakhand
28 West Bengal
29 Andaman and Nicobar Islands
30 Chandigarh
31 Dadra and Nagar Haveli
32 Daman and Diu
33 Delhi
34 Jammu and Kashmir
35 Ladakh
36 Lakshadweep
37 Puducherry